Deviation Of Error Research Articles

Endoscopic-Assisted Mandibular Angle Revision Using Patient-Specific PEEK Implants: Surgical Accuracy and Aesthetic Outcomes in Over-Resected Mandibles.

Mandibular angle osteotomy (MAO) is a popular procedure to improve facial aesthetics, however, over-resection of the mandibular angle can lead to both functional and aesthetic challenges. Precision is essential in restoring these over-resected mandibles to achieve balanced outcomes. Polyetheretherketone (PEEK) implants offer biocompatibility, durability, and customization potential, making them valuable for achieving precise and predictable results. This study examines the efficacy of PEEK patient-specific implants (PSIs) in endoscopic-assisted mandibular angle revision (MAR) surgery, assessing improvements in surgical accuracy and aesthetic outcomes. We retrospectively analyzed 24 patients (39 mandibles) who underwent MAR from January 2019 to December 2023. Surgical accuracy was measured by comparing planned and achieved PSI positions using root mean square error (RMSE) and maximum deviation (MaxD). Patient satisfaction was assessed using the FACE-Q questionnaire, administered preoperatively and at least six months postoperatively, with correlations drawn between implant fit accuracy and FACE-Q scores changes. The average RMSE ranged from 0.1166 to 0.3149mm, with 96% of PSIs showing deviations under 0.3 mm. FACE-Q scores for lower facial appearance and psychological well-being improved significantly, form 42.50 ± 5.28 to 56.08 ± 4.58 and 61.50±4.31 to 70.58 ± 6.46, respectively. A strong negative correlation was observed between implant fit accuracy (RMSE, MaxD) and FACE-Q score improvements related to appearance. Endoscopic-assisted MAR with PEEK PSIs offers precise correction and significant aesthetic improvements for patients with over-resected mandibles. This technique demonstrated high surgical accuracy and favorable patient-reported outcomes. Future studies on a larger scale are recommended to validate these findings. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal Data

The cultivation of saline–alkali-tolerant peanut (Arachis hypogaea L.) varieties can effectively increase grain yield in saline–alkali land. However, traditional assessment methods are often cumbersome and time consuming. To rapidly identify saline–alkali stress-tolerant peanut varieties, this research proposed a saline–alkali stress tolerance evaluation method based on deep learning and multimodal data. Specifically, the research first established multimodal datasets for peanuts at different growth stages and constructed a saline–alkali stress score standard based on unsupervised learning. Subsequently, a deep learning network called BO-MFFNet was built and its structure and hyperparameters were optimized by the Bayes optimization (BO) algorithm. Finally, the point prediction of the saline–alkali stress score were carried out by using the Gaussian process regression model. The experimental results show that the multimodal method is superior to the single-modal data and the BO algorithm significantly improves the performance of the model. The root mean squared error and relative percentage deviation of the BO-MFFNet model are 0.089 and 3.669, respectively. The model effectively predicted the salt–alkali stress tolerance of five varieties, and the predicted results were Huayu25, Yuhua31, Yuhua33, Yuhua32, and Yuhua164 from high to low. This research provides a new method for assessing crop tolerance under extreme environmental stress.

Fast frequency and phase synchronization of high-stability oscillators with 1 PPS signal from satellite navigation systems

Abstract In this paper, we propose a novel algorithm for fast frequency and phase synchronization of high-stability oscillators synchronized with 1 PPS signal from satellite navigation systems. The algorithm uses a model of a control object in the space of state variables and controls the frequency of an oscillator operating in a phase-locked loop. A new element is the introduction to the theoretical analysis and the design process, the time of entering synchronization. Currently, the literature lacks theoretical analysis and design methodology that considers the impact of the synchronization time on the choice of the steering algorithm and its parameters. All the data needed to determine the numerical values of the model were found experimentally for three different classes of control objects. Short synchronization times, a detailed description of the design methodology, and the use of values measured in the real system distinguish the proposed algorithm from the solutions described in the literature. The effect of optimization was achieved thanks to the algorithm’s two-stage operation. In the first stage, the algorithm aims to minimize the phase error quickly. The best solution for this stage is Sliding Mode Control (SMC). In the second stage, the algorithm strives to maximize the control quality, understood as minimizing the values of Maximum Time Interval Error (MTIE) and Time Deviation (TDEV). The Model Predictive Control (MPC) and Linear-Quadratic Regulator (LQR) optimal control algorithms were used at this stage. The paper also investigated the influence of the tuning parameters of these algorithms (weights as a function of cost) on the long-term behavior of the control system.

Indoor Multi-Different-Wall Path Loss Prediction Model Using Adaptive Neuro-Fuzzy Inference System

The efficiency of wireless systems is heavily dependent on the precision of the propagation channel model. Accurate prediction of signal path loss is an essential process in planning and optimizing radio networks. In this paper, a new indoor path loss prediction model - utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach - is proposed. The proposed model takes into consideration the effect of different indoor barriers or wall types, in addition to other essential factors, on the propagation channel to obtain precise prediction in buildings with hybrid walls or partitions. On-site recorded WiFi data is used to build and verify the proposed model. To minimize modeling errors, a hybrid training method is employed, and the input membership functions of the model are optimized. The proposed ANFIS model is compared with the Cost-231 propagation model after it has been calibrated using a multilinear regression approach. The comparison indicates the superiority of the proposed model in terms of precision and accuracy. The obtained results show that the new model has a lower root mean square error and standard deviation with a higher correlation factor of 98%. The proposed ANFIS model produces precise indoor WiFi prediction and promotes accurate forecasting of other frequency bands - such as UMTS and GSM - in buildings with different interior structures.

Design of Geothermal Groundwater Heating and Cooling Plant: A Trial Study in Eleme Fertilizer Company in Niger Delta Region

A geothermal groundwater heating and cooling plant consisting of a heat pump and heat exchanger has been designed. Geothermal heating and cooling systems play a crucial role in the decarbonization of the environment, helping to prevent global warming and promote energy savings due to the reduced energy required to power the plant, which in turn generates more geothermal energy for the heating and cooling needs of the fertilizer company. This study considered Eleme Fertilizer as a case study for the scaled-up and installation of the prototype designed of the geothermal groundwater heating and cooling plant. Both manual and software simulation designs were conducted based on material and energy balance principles. Error analysis and deviations were used to validate the design results. The results of the plant's unit design showed that the power drive, overall efficiency, and coefficient of performance for heating and cooling were 354.1 kW, 402%, and 2.82 & 1.05, respectively. For the heat exchanger, design type 10-E-01, the values for heat duty, overall heat transfer coefficient, exchanger area, and tube pressure drop were 309.5 kW, 0.2104 kW/m²°C, 60.32 m², and 390 MPa, respectively. Error analysis conducted on the design work showed negligible values of RMSE (0.02, 0.025, 20 and 0.656) and deviation (0.014, 0.05, 0.05, 0.002) for coefficient of performance of heating and cooling, heat pump efficiency and the drive power of the plant respectively. The values obtained from the design of the units in the geothermal plant were reasonable and are thus recommended for academic and industrial applications

Most Cited Publications on Root Resorption of Permanent Teeth

Introduction: Root resorption of permanent teeth is a complex phenomenon, often presenting clinical challenges. The aim was to identify and analyze the most cited publications related to root resorption of permanent teeth. Method: A bibliographic search of all citations related to root resorption of permanent teeth published in scientific journals. Only publications with 100 citations or more were included, divided into four categories: experimental and review studies related to external or internal root resorption. A descriptive statistical analysis was conducted, including standard deviation and margin of error. Results: Sixty-nine (0.49%) publications, 48 experimental, and 21 review studies met the inclusion criteria. Of the 48 most cited experimental research publications, 48 (100%) pertained to external root resorption. Of the 21 most cited review publications, 19 (90.48%) pertained to external root resorption, and 2 (9.52%) to internal root resorption.

An efficient optimized encryption and compression techniques to improve medical image security and transmission in the cloud

As digital technology for illness diagnosis and analysis has advanced, medical images are sent over the Internet. Cloud computing plays a major role for low-cost data storage and data sharing. In the healthcare industry, data security and privacy are key issues with cloud computing. It is imperative that healthcare professionals make sure that patient data is safe against hackers, unauthorized access, and thrift. To secure the confidential data and store the huge amount of data, encryption and compression techniques are used. This paper provides an enhanced optimized encryption and efficient hybrid compression strategies based on cloud environment. The proposed model involves various operations such as generate optimal key, encryption, compression, decompression and decryption. In order to transfer the data using high speed cloud data retrieval, we initially propose the Huffman Fano Hybrid Entropy approach. In the next step, the proposed model performs Elliptic Curve Coding based encryption technique to secure compressed medical image transmission. Here the shared secret keys are generated optimally by dynamic group based cooperative optimization algorithm, which makes use of the encryption quality measures and is called DGBCO–ECC model. On the receiving end, the image is decrypted and decompressed. The proposed model performance is validated by exploiting various parameters namely Mean Square Error, Standard Deviation of the Mean Error, Universal image quality index, Structural Similarity Index, Entropy, Peak Signal to Noise Ratio, Compression Ratio, Data Rate Saving and compression time. When the experimental outcome is contrasted with existing methods, it is found to perform better.

Application of standard keratometry values obtained from keratotopographic mapping data to calculate the optical power of a multifocal intraocular lens

Purpose of the study is to determine the zones of standard keratometry according to keratotopography data, which will allow for more accurate calculation of multifocal IOL using 10 formulas.Material and methods. The study included 55 patients (55 eyes) who underwent phacoemulsification of cataract or refractive lensectomy with femtolaser accompaniment, implantation of multifocal IOL (Acrysof IQ PanOptix) and achieved the target refraction at different distances. Retrospective calculation of the optical power of the IOL was carried out using biometric data from OA-2000 and keratometric indicators of Pentacam (zones from 0.5 mm to 5 mm in increments of 0.5 mm on the Axial/Sagittal map centered on the apex and pupil) using 10 formulas (SRK/T, Holladay 1, Holladay 2, Haigis, Hoffer Q, Barrett 2 Universal, Olsen, Kane, EVO ver. 2.0, Hill RBF ver. 3.0). For each combination of zone/keratometry value/formula, the average error of postoperative predicted refraction, its difference from zero (Wilcoxon criterion), the median value taking into account the sign, the mean (MAE) and median (MedAE) absolute errors in calculating the spherical equivalent of the IOL, the standard deviation of the mean absolute error (SD) were calculated.Results. All formulas had a shift to myopic refraction, except for the Haigis formula, which shifted towards hyperopia. The absence of a significant difference from zero was shown only by the Kane formula in zones 3.5, 4.5–5.0 mm when centered on the apex and in zones 0.5, 1.5, 2.5–5.5 mm when centered on the pupil. The highest values of MAE were found in Haigis and Olsen formulas, and the minimum values were observed in most formulas in the 4.5–5.0 mm zones. The lowest MedAE values in all ranges were shown by the formulas Kane, EVO, Holladay 1 and Holladay 2. The minimum SD values were found for the formulas Kane, EVO, Holladay 1 and Holladay 2.Conclusion. The Kane formula turned out to be the most accurate in the 4.5–5.0 mm zone. This is followed by the EVO 2 and Holladay 1 formulas in the 4.0 mm zone. The Haigis formula turned out to be the least accurate. The remaining formulas can be recommended for use with SimK Pentacam data in 4.0–5.0 mm zones.

Advanced Neural Network-Based Load Frequency Regulation in Two-Area Power Systems

In this paper, enhancing dynamic performance in power systems through load frequency control (LFC) is explored across diverse operating scenarios. A new Neural Network Model Predictive Controller (NN-MPC) specifically tailored for two-zone load frequency power systems is presented. ” Make your paper more scientific. The NN-MPC marries the predictive accuracy of neural networks with the robust capabilities of model predictive control, employing the nonlinear Levenberg-Marquardt method for optimization. Utilizing local area error deviation as feedback, the proposed controller's efficacy is tested against a spectrum of operational conditions and systemic variations. Comparative simulations with a Fuzzy Logic Controller (FLC) reveal the proposed NN-MPC's superior performance, underscoring its potential as a formidable solution in power system regulation.

Improvement of NC Program Quality based on Shape Generation Motions and Feed Drives for Five-Axis CNC Machine Tools

Five-axis Computer Numerical Control (CNC) machine tools, integrated with Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems, are used to machine complex parts and reduce trials and errors. However, these machine tools still rely on Numerical Control (NC) programs and often lack accuracy and precision due to poor quality when implemented in the machine. This research aims to enhance the quality of NC programs for five-axis CNC machine tools by focusing on shape generation motions and a closed-loop feed drive system with Proportional-Integral-Derivative (PID) control. The individual motions were mathematically described using 4x4 transformation matrices, incorporating kinematic motion deviations, end mill geometry, machining parameters, and cutting forces derived from virtual machining. Additionally, a closed-loop feed drive system with PID control was integrated with the new position and angular data of each axis from the shape generation motions model. The new NC programs were validated by machining an S-shaped part and measuring dimensional errors at 64 points before and after using a Coordinate Measuring Machine (CMM). The results indicate a substantial reduction in the standard deviations of form and angular errors within the NC program quality, totaling approximately 80.73%. Reductions are demonstrated in the standard deviations for the X, Y, A, and B axes, with decreases of 76.83%, 95%, 82.40%, and 68.72%, respectively indicating a significant improvement in the overall quality of the NC program.

Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device.

Objectives: Respiratory rate (RR) is a clinical measure of breathing frequency, a vital metric for clinical assessment. However, the recording and documentation of RR are considered to be extremely poor due to the limitations of the current approaches to measuring RR, including capnography and manual counting. We conducted a validation of the automatic RR measurement capability of AcuPebble RE100 (Acurable, London, UK) against a gold-standard capnography system and a type-III cardiorespiratory polygraphy system in two independent prospective and retrospective studies. Methods: The experiment for the prospective study was conducted at Imperial College London. Data from AcuPebble RE100 (Acurable, London, UK) and the reference capnography system (Capnostream™35, Medtronic, Minneapolis, MN, USA) were collected simultaneously from healthy volunteers. The data from a previously published study were used in the retrospective study, where the patients were recruited consecutively from a standard Obstructive Sleep Apnea (OSA) diagnostic pathway in a UK hospital. Overnight data during sleep were collected using the AcuPebble SA100 (Acurable, London, UK) sensor and a type-III cardiorespiratory polygraphy system (Embletta MPR Sleep System, Natus Medical, Pleasanton, CA, USA) at the patients' homes. Data from 15 healthy volunteers were used in the prospective study. For the retrospective study, 150 consecutive patients had been referred for OSA diagnosis and successfully completed the study. Results: The RR output of AcuPebble RE100 (Acurable, London, UK) was compared against the reference device in terms of the Root Mean Squared Deviation (RMSD), mean error, and standard deviation (SD) of the difference between the paired measurements. In both the prospective and retrospective studies, the AcuPebble RE100 algorithms provided accurate RR measurements, well within the clinically relevant margin of error, typically used by FDA-approved respiratory rate monitoring devices, with the RMSD under three breaths per minute (BPM) and mean errors of 1.83 BPM and 1.4 BPM, respectively. Conclusions: The evaluation results provide evidence that AcuPebble RE100 (Acurable, London, UK) algorithms produce reliable results and are hence suitable for overnight monitoring of RR.

Multi-Global Navigation Satellite System (GNSS) real-time tropospheric delay retrieval based on state-space representation (SSR) products from different analysis centers

Abstract. The troposphere plays an important role in a range of weather and various climate changes. With the development of the Global Navigation Satellite System (GNSS), the zenith tropospheric delay (ZTD) retrieval using GNSS technology has become a popular method. Research on ZTD accuracies of state-space representation (SSR) corrections from different analysis centers derived from real-time precise point positioning (RT-PPP) is important for Earth observation correction, meteorological disaster forecasting, and warning with the increasing abundance of state-space representation (SSR) products obtained by the International GNSS Service (IGS) analysis center. Therefore, accuracies and availability of real-time orbits and clock errors obtained by the Chinese Academy of Sciences (CAS), GMV Aerospace and Defense (GMV), Centre National d'Etudes Spatiales (CNE), and Wuhan University (WHU) are evaluated, and the RT positioning performance and ZTD accuracies are analyzed for Global Positioning System (GPS), Galileo (GAL), and BeiDou Navigation Satellite System-3 (BDS3) satellites. The results indicate that CAS has the higher satellite availability, providing SSR corrections for 82 GPS, Galileo, and BDS3 satellites. The accuracies of GPS, Galileo, and BDS3 orbits are best at WHU, CAS, and WHU with values of 5.57, 5.91, and 11.77 cm, respectively; the standard deviations (SDs) of clock error are all better than 0.22, 0.19, and 0.55 ns, and the root mean square errors (RMSEs) are better than 0.54, 0.32, and 1.46 ns. CAS has the best signal-in-space ranging errors (SISREs) followed by WHU, while CNE and GMV are worse. In the RT-PPP test, convergence times for CAS and WHU are 14.9 and 14.4 min, respectively, with 3D positioning accuracy for both of around 3.3 cm, which is better than for CNE and GMV. Among them, WHU SSR has the higher accuracy of RT-PPP-derived ZTD, with an RMSE of 6.06 mm and desirable availability with a completeness rate of 89 %.

Assessing the Impact of Force Feedback in Musical Knobs on Performance and User Experience

This paper examined how rotary force feedback in knobs can enhance control over musical techniques, focusing on both performance and user experience. To support our study, we developed the Bend-aid system, a web-based sequencer with pre-designed haptic modes for pitch modulation, integrated with TorqueTuner, a rotary haptic device that controls pitch through programmable haptic effects. Then, twenty musically trained participants evaluated three haptic modes (No-force feedback (No-FF), Spring, and Detent) by performing a vibrato mimicry task, rating their experience on a Likert scale, and providing qualitative feedback in post-experiment interviews. The study assessed objective performance metrics (Pitch Error and Pitch Deviation) and subjective user experience ratings (Comfort, Ease of Control, and Helpfulness) of each haptic mode. User experience results showed that participants found force feedback helpful. Performance results showed that the Detent mode significantly improved pitch accuracy and vibrato stability compared to No-FF, while the Spring mode did not show a similar improvement. Post-experiment interviews showed that preferences for Spring and Detent modes varied, and the applicants provided suggestions for future knob designs. These findings suggest that force feedback may enhance both control and the experience of control in rotary knobs, with potential applications for more nuanced control in DMIs.

Prediction of crippling load of I-shaped steel columns by using soft computing techniques

This study is primarily aimed at creating three machine learning models: artificial neural network (ANN), random forest (RF), and k-nearest neighbour (KNN), so as to predict the crippling load (CL) of I-shaped steel columns. Five input parameters, namely length of column (L), width of flange (bf), flange thickness (tf), web thickness (tw) and height of column (H), are used to compute the crippling load (CL). A range of performance indicators, including the coefficient of determination (R2), variance account factor (VAF), a-10 index, root mean square error (RMSE), mean absolute error (MAE) and mean absolute deviation (MAD), are used to assess the effectiveness of the established machine learning models. The results show that all of the three ML (machine learning) models can accurately predict the crippling load, but the performance of ANN is superior: it delivers the highest value of R2 = 0.998 and the lowest value of RMSE = 0.008 in the training phase, as well as the highest value of R2 = 0.996 and the smaller value of RMSE = 0.012 in the testing phase. Additional methods, including rank analysis, reliability analysis, regression plot, Taylor diagram and error matrix plot, are employed to assess the models’ performance. The reliability index (β) of the models is calculated by using the first-order second moment (FOSM) technique, and the result is compared with the actual value. Additionally, sensitivity analysis is performed to check the impact of the input variables on the output (CL), finding that bf has the greatest impact on the crippling load, followed by tf, tw, H and L, in that order. This study demonstrates that ML techniques are useful for developing a reliable numerical tool for measuring the crippling load of I-shaped steel columns. It is found that the proposed techniques can also be used to predict other kinds of failures as well as different kinds of perforated columns.

Investigation of the impact of product thickness and strain on cold forging processes

This study investigates the impact of sheet metal thickness and strain generated on a cold forging process. A cold forging die was manufactured based on a numerically modelled product design that considered stress concentration die profiles. Experiments involved cold forging various sheet metals (lead, brass, aluminium, and steel) with thicknesses ranging from 2mm to 6mm to assess their influence on the thickness and strain. An assumption was formulated using Solidworks to determine the appropriate sheet metal thickness based on the material type. This assumption, based on volume considerations, showed practical applicability for selecting sheet metals with thicknesses close to those designed in the numerical modelling. The results revealed that softer materials, such as lead and aluminium, closely approximated the design thickness, particularly in the central region of the die profile, with error deviations of 1.14 % and 4.36 %, respectively. In contrast, harder materials demonstrated larger deviations from the design, with minimum errors of 4.94 % for brass and 5.73 % for steel. All sheet metals underwent compressive strain in the central region of the die profile due to the stamping operation. This work demonstrates a practical approach for selecting sheet metals based on material and thickness considerations, providing insights into the behaviour of different materials during the cold forging process.

Cuff-less wearable biosensor in continuous noninvasive human radial artery pulse waveform and blood pressure measurement using self-mixing interferometry

In this paper, we propose a compact, wearable biosensor for the noninvasive measurement of human radial artery pulse waveform curve (PWC) and blood pressure (BP). In this system, self-mixing interferometry (SMI) technology is employed to measure the weak arterial vascular deformation, enabling accurate PWC retrieval. Based on the reconstructed PWC features, BP values are precisely estimated by means of deep learning method. Here continuous wavelet transform (CWT), enabling visualization of the relationship between the SMI signal temporal frequency components and the PWC characteristics, is highlighted for PWC flipping points seeking and convolutional neural network (CNN) input parameter acquisition. For the first time, a novel deep learning network preprocessing method is proposed that allows direct feature extraction from the CWT scalogram of SMI signal without the complicated PWC reconstruction algorithm. The robustness and accuracy of our device are validated by a series of clinical measurements, mean absolute error (MAE) and standard deviation (STD) values are calculated and compared with the existing models. We approach minimal BP estimation results (MAE ± STD) of 1.41 ± 1.89 mmHg for systolic blood pressure (SBP) and 1.78 ± 2.01 mmHg for diastolic blood pressure (DBP), respectively. The luxuriant novelties and remarkable performance clearly demonstrate our wearable sensor’s great potential in BP monitoring, and other clinical applications.

Accuracy of 14 intraocular lens power calculation formulas in extremely long eyes.

To compare the accuracy of 14 formulas in calculating intraocular lens (IOL) power in extremely long eyes with axial length (AL) over 30.0mm. In this retrospective study, 211 eyes (211 patients) with ALs > 30.0mm were successfully treated with cataract surgery without complications. Ocular biometric parameters were obtained from IOLMaster 700. Fourteen formulas were evaluated using the optimized A constants: Barrett Universal II (BUII), Kane, Emmetropia Verifying Optical (EVO) 2.0, PEARL-DGS, T2, SRK/T, Holladay 1, Holladay 2, Haigis and Wang-Koch AL adjusted formulas (SRK/Tmodified-W/K, Holladay 1modified-W/K, Holladay 1NP-modified-W/K, Holladay 2modified-W/K, Holladay 2NP-modified-W/K). The mean prediction error (PE) and standard deviation (SD), mean absolute errors (MAE), median absolute errors (MedAE), and the percentage of prediction errors (PEs) within ± 0.25 D, ± 0.50 D, ± 1.00 D were analyzed. The Kane formula had the smallest MAE (0.43 D) and MedAE (0.34 D). The highest percentage of PE within ± 0.25 D was for EVO 2.0 (37.91%) and the Holladay 1NP-modified-W/K formulas (37.91%). The Kane formula had the highest percentage of PEs in the range of ± 0.50, ± 0.75, ± 1.00, and ± 2.00 D. There was no significant difference in PEs within ± 0.25, ± 0.50 ± 0.75 and ± 1.00 D between BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas (P > .05) by using Cochran's Q test. The Holladay 2modified-W/K formula has the lowest percentage of hyperopic outcomes (29.38%). The BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas have comparable accuracy for IOL power calculation in eyes with ALs > 30.0mm.

A New Intraocular Lens Power Formula Integrating an Artificial Intelligence-Powered Estimation for Effective Lens Position Based on Chinese Eyes.

To develop and evaluate a new intraocular lens (IOL) formula based on Chinese eyes. A training dataset of 709 eyes undergoing uneventful cataract surgery was used to train the algorithm for effective lens position estimation. The algorithm was then integrated with Gaussian optics to develop the new IOL formula (Jin-AI). From the same center, 177 eyes served as an internal test dataset. An independent dataset of 557 eyes served as an external test dataset. The standard deviation (SD) of prediction errors was compared among the Jin-AI formula, traditional third-generation formulas (SRK/T, Holladay 1, Hoffer Q), and newer generation formulas (Kane, Barrett Universal II [BUII], Hill-radial basis function [RBF] 3.0, and PEARL-DGS). In the internal test dataset, the Jin-AI formula showed the lowest SD (0.25 D), followed by the BUII (0.31 D), Kane (0.33 D), and PEARL-DGS (0.33 D) formulas. In the external test dataset, the Jin-AI, Kane, and PEARL-DGS formulas had the lowest SD (0.38 D), followed by the BUII (0.39 D), Hill-RBF 3.0 (0.39 D), SRK/T (0.45 D), Holladay 1 (0.48 D), and Hoffer Q (0.48 D) formulas. The SD of the Jin-AI formula was significantly lower than the third-generation formulas and comparable to the four newer generation formulas. Predictive accuracy of the Jin-AI formula was similar to the newer generation formulas across all axial length, keratometry, and anterior chamber depth ranges. The new formula has exhibited good performance in predicting postoperative refractions. Its overall predictive accuracy was better than the third-generation formulas and comparable to the newer generation ones. The Jin-AI formula could be a reliable alternative for IOL power calculation in Chinese.

Precision straightness and displacement measurement based on phase-modulated interferometric scheme with different modulation depths

In this study, a novel straightness and displacement measurement method using a Wollaston prism-sensing phase-modulated homodyne interferometer (WPHI) is proposed to accurately measure straightness errors and the position of linear stages. In the proposed WPHI, a Wollaston prism assembly along with a semi-reflector serves as a sensing element to generate two straightness and one displacement measuring beams. By using an electro-optic phase modulator to modulate the linearly polarized laser beam at 45° to its optic axis, different modulation depths are introduced into the straightness and displacement measuring beams to obtain phase-modulated interference signals. Thus, the straightness error can be directly obtained from the change in the optical path difference between the two measuring beams, thereby avoiding any additional errors introduced by separately measuring the changes in the optical path of each beam. Moreover, a 2 × 2 array photodetector is used to simultaneously measure the displacement and rotational angles of stage to compensate for the influence of rotational errors on the displacement result. The experimental results showed that within a range of 4 m, the standard deviations of the straightness error and displacement measurements between the proposed WPHI and a commercial interferometer were 0.50 and 0.59 μm, respectively. Thus, the proposed WPHI has substantial applications in the fields of ultraprecision machine-tool control, precision motion stage testing, and displacement sensor calibration.

Determining the optimum severity of damage using model performance analysis methods

In this work, we have considered a cantilever beam with a rectangular plastic cross-section, reinforced by four wire reinforcements made of ductile Iron. It was studied using numerical resolution based on theoretical formulae and then simulated using Solidworks software. A comparison of the results on the frequency ratios between the theoretical and numerical by Solidworks models gave a low error of 1.389% with a high correlation coefficient of around 0.959. Then, two top reinforcements inside the beam were cut in order to study the behavior of the intact beam and the damaged beam. Using the simulation results, we found an accurate damage severity using a mathematical method based on error deviation indicators or Model Performance Analysis (MPA).